CRB-Constrained Sum-Rate Maximization for Movable Antenna-Enabled IRS-ISAC Systems

This paper investigates the movable antenna (MA)enabled intelligent reflecting surface-aided integrated sensing and communication (IRS-ISAC) systems, where an MA-enabled base station (BS) provides downlink communications for single antenna communication users (CUs) and direction-of-arrival (DOA) estimation for one target with the assistance of an IRS. To enhance the multi-user multiple-input single-output (MU-MISO) communication while ensuring the accuracy of the DOA estimation, we derive the Cramér-Rao bound (CRB) with respect to the DOA and formulate the CRB-constrained sum-rate maximization problem, and a product Riemannian manifold optimization (PRMO) method is proposed to tackle the non-convex problem. Specifically, a product Riemannian manifold space (PRMS) is constructed as the solution space, and the penalty method and Riemannian Broyden-Fletcher-Goldfarb-Shanno (RBFGS) algorithm are exploited to obtain a feasible solution. Simulation results verify the advantages of deploying MA in the IRS-ISAC systems and the effectiveness of the proposed PRMO method.